Geojson Testing

Setup

Packages

# Packages
require(ggplot2)
require(plotly)

Loading required package: plotly
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: 㤼㸱plotly㤼㸲

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    last_plot

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    filter

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    layout

require(geojsonio)

Loading required package: geojsonio
package 㤼㸱geojsonio㤼㸲 was built under R version 4.0.3
Attaching package: 㤼㸱geojsonio㤼㸲

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require(sp)

Loading required package: sp
package 㤼㸱sp㤼㸲 was built under R version 4.0.3

require(sf)

Loading required package: sf
package 㤼㸱sf㤼㸲 was built under R version 4.0.3Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

require(rvest)

Loading required package: rvest
Loading required package: xml2

require(RSelenium)

Loading required package: RSelenium
package 㤼㸱RSelenium㤼㸲 was built under R version 4.0.3

require(htmltools)

Loading required package: htmltools

require(ggmap)

Loading required package: ggmap
package 㤼㸱ggmap㤼㸲 was built under R version 4.0.3Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
Please cite ggmap if you use it! See citation("ggmap") for details.

Attaching package: 㤼㸱ggmap㤼㸲

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Data

# Download file for state info

url = "https://opendata.arcgis.com/datasets/5f45e1ece6e14ef5866974a7b57d3b95_1.geojson"

file = "NJ_counties.geojson"

download.file(url,file)

rm(url)

# Convert file to sf object 

file = "NJ_counties.geojson"

NJ_Counties <-
  geojson_sf(file)
rm(file)

# Clean Data 

NJ_Counties_Cleaned <- 
  NJ_Counties %>%
  transmute(
    county = COUNTY,
    CO = CO,
    pop = POP2010,
    popdensity = POPDEN2010,
    Shape_Length = Shape_Length,
    Shape_Area = Shape_Area,
    GNIS = GNIS
  )

#get page source from website

gc()
driver <- rsDriver(browser = c("firefox"), port = 44454L)
remote_driver <- driver[["client"]] 
remote_driver$navigate("https://www.childrens-specialized.org/locations-directory/?")

page <- remote_driver$getPageSource()

# Retrieve information from directory

Xpathgen1 = "/html/body/div[1]/div/div/div[2]/div/div[2]/div["

Xpathgen2 = "]/div/div[2]/article"

Hosinfo <- data.frame()

for (i in 1:15){
  XPath <- paste(Xpathgen1,i,Xpathgen2,sep = "")
  Node <- page[[1]] %>% 
    read_html() %>%
    html_nodes(xpath = XPath)
  name <-
    Node[[1]] %>%
    html_node("h2") %>%
    html_text()
  address <-
    Node[[1]] %>%
    html_node("h3") %>%
    html_text() %>%
    gsub(pattern = "\n *",replacement = " ", x = .)
  for (i in 1:7){
  XPathday <- paste(XPath,"/div[",i,"]",sep = "")
  day <- page[[1]] %>% 
    read_html() %>%
    html_nodes(xpath = XPathday) %>%
    html_attr("class") %>%
    grep("-Hours",x = .,value = TRUE) %>%
    gsub("-Hours","",x = .)
  times <-
    page[[1]] %>% 
    read_html() %>%
    html_nodes(xpath = XPathday) %>%
    html_node("h3") %>%
    html_text()
  assign(day,times)
  }
  row = data.frame(name,address,Monday,Tuesday,Wednesday,Thursday,Friday,Saturday,Sunday)
  Hosinfo <-  rbind(Hosinfo,row)
}

# Write csv file 

write.csv(Hosinfo, "Hospitals.csv")

# Data Wrangling

Hosinfo <- read.csv("Hospitals.csv")

pattern <- "([0-1]*[0-9]:[0-5][0-9] *[AaPp][Mm][-to ]+[0-1]*[0-9]:[0-5][0-9] *[AaPp][Mm])" # Pattern to extract first time range

Hosloc <- 
  Hosinfo %>%
  select(name,address) %>%
  mutate_geocode(address) # Requires google API key

rm(pattern)

write.csv(Hosloc,"Hospitalsloc.csv")

Graphs

# Map of NJ 

NJ <-
  NJ_Counties_Cleaned %>%
  ggplot() +
  geom_sf() +
  theme(legend.position = "None") +
  theme(axis.ticks = element_blank(),
        axis.text = element_blank(),
        title = element_text(size = 9)) +
  labs(y = "",
       x = "",
       title = "Map of NJ divided by county")

ggplotly(NJ)

NJ

plot(NJ_Counties_Cleaned)

# Population heat maps of NJ using 2010 population data

NJ <-
  NJ_Counties_Cleaned %>%
  ggplot() +
  scale_fill_gradientn(colours=c("white", "blue")) +
  geom_sf(aes(fill = pop)) +
  theme(legend.position = "None") +
  theme(axis.ticks = element_blank(),
        axis.text = element_blank(),
        title = element_text(size = 9)) +
  labs(y = "",
       x = "",
       title = "2010 Population Heatmap")


ggplotly(NJ)

NJ

plot(NJ_Counties_Cleaned["pop"])

# Location of Hospitals in NJ on heatmap

Hosloc <- read.csv("Hospitalsloc.csv")

# Only need out patient centers 

Hosloc <-
  Hosloc %>%
  filter(grepl("Outpatient", name))


NJ <-
  NJ_Counties_Cleaned %>%
  ggplot() +
  scale_fill_gradientn(colours=c("white", "steelblue")) +
  geom_sf(aes(fill = pop)) +
  geom_point(data = Hosloc,aes(x = lon,y = lat,shape = "square", label = name,color = "orange")) +
  theme(legend.position = "None") +
  theme(axis.ticks = element_blank(),
        axis.text = element_blank(),
        title = element_text(size = 9, family = "serif")) +
  labs(y = "",
       x = "",
       title = "2010 Population Heatmap\nwith CSH Outpatient Center")

Ignoring unknown aesthetics: label

ggplotly(NJ,tooltip = "name")

NJ

NJ %>%
ggsave(file="PopheatmapNJ.png", plot = ., width=3, height=4, dpi=300)